D. Sivakumar

Analysis of gas metal arc welding process using GA tuned fuzzy rule based system

The weld quality is generally controlled by the welding parameters. In gas metal arc welding process, the welding parameters are inter related and the adjustment of one parameter may affect another parameter and hence it is considered as a non-linear process. The non-linear nature of the welding system makes it difficult to implement a conventional control method. Fuzzy logic control is an attractive alternative approach. The performance of fuzzy controller will be very much dependent on the knowledge provided to the system. Therefore in recent years, more research has been devoted to augment the approximate reasoning method of fuzzy systems with genetic algorithms. In the present work, genetic algorithm tuned conventional controller is implemented for gas metal arc welding system. Its performance is compared with that of genetic algorithm based fuzzy logic controller.

Dynamic matrix controller design for performance study of an interacting coupled tank MIMO process

Model predictive control (MPC) is the class of advanced control techniques. A primary advantage to this approach is the explicit handling of constraints. MPC utilizes an internal model to predict system dynamic behaviour over a finite horizon. MPC is a discrete-time form of control, so inaccuracies in predicted behavior are corrected at the next control interval. This technique makes the control of processes to become more efficient and cost effective. Most of its applications are in the refining, petrochemical industries and in other chemical plants. Dynamic Matrix Control(DMC) is a kind of model predictive control technique based on step response model of the process. In this paper, the dynamic matrix control algorithm is implemented on coupled tank system and control quality has been analyzed using a simulation model with different setting parameters. From the simulation results it has been observed that dynamic matrix control algorithm can achieve good results with accuracy.

Design of an optimal PI controller for a non-linear process using genetic algorithm

Many industrial processes have to satisfy different criteria to achieve better quality product and performance. These multiple performance criteria need to be optimised simultaneously. However, a suitable optimal solution meeting the entire criteria can hardly be found since these criteria are conflicting. Compared to conventional optimisation techniques, genetic algorithms (GAs) are well suited to solve optimisation problems that involve multiple performance criteria. This paper presents the application of GA to optimise the PI controller parameters for a non-linear process. Least square estimation (LSE) method is used to estimate the parameters of the process. All the PI controllers which stabilise the given plant in the specified ranges are designed using the estimated plant parameters. GA searches the optimal controller parameters within this stabilising set. Experimental results are provided, showing the effectiveness of the multiple performance criteria tuned controller for a nonlinear process.

Multiloop Adaptive Controllers for a Nonlinear Interacting Coupled Tank Process

This paper presents the design and implementation of Direct Adaptive Controller (DAC) to control the level of liquid in a nonlinear two tank interacting process. Mean Square error is chosen as minimization criteria for the design of the controller. The objective of this work includes performance comparison of Adaptive MIT and Adaptive PI controller with two tank interacting process. The Constant PI, Adaptive MIT and Adaptive PI controllers are implemented for four different operating regions. White box model of the process is used in this work. The design and simulation studies are carried out in MATLAB/SIMULINK. Keywords— Nonlinear system, Interacting two tank system, MRAC, MIMO, Mathematical modeling, White box model

Performance and stability analysis of an adaptive controller for a non interacting system

This paper deals with continuous time adaptive control of a nonlinear two tank non-interacting system. To control the level in the tank, controllers with two degrees of freedom are designed and implemented. The controllers designed are based on gradient approach and the stability approach, resulting the closed loop control system to have asymptotic tracking. Both the controllers are based on the minimization of mean square error. Even though these controls are non-identifier type, to have apriori knowledge and for simulation purpose the nonlinear system is modeled as SOP using real time data. The performances of the controllers are tested in Simulation.

Design and implementation of RST controllers for a nonlinear system

This paper aims at developing an efficient controller for a nonlinear system. This necessitates identifying a near accurate model using the data acquired through real time experimentation conducted on the laboratory level process. An offline parametric estimation method is used to obtain discrete-time models for various operating regions. Three different digital polynomial controllers are designed and implemented to achieve the desired dynamics of the reference model. In order to obtain further improvement in the desired performance closed loop poles are relocated by Pole placement technique. The objective of this work also includes a comparative analysis of performances of the chosen system when implementing all these four strategies under different operating regions. The design and simulation studies are carried out in MATLAB/SIMULINK platform.

Multimodel approach to the design of scheduling controllers for a class of nonlinear system

Nonlinearity emerges due to parameter uncertainties, constraints and also due to the fact that processes are subjected to disturbances quite often. This nonlinearity behavior leads to operate the industrial processes at different regions. These problems can be solved effectively by implementing a controller based on multimodel approach. In order to design such controllers it is always mandatory to properly identify the process. In our work the chosen process is identified using real time data acquired from the laboratory level process station. Though the design of constant gain controllers are the most popular option to solve the industrial control problems, but it fails to show better results for nonlinear systems. This paper aims at developing design strategy of scheduling controller by extending Polynomial Dynamic Control approach. The parameters of scheduling controller are self tuned in order to accommodate the system uncertainties. The goal of the paper is to analyze the reference tracking efficiency and external disturbance rejection when implementing these two approaches in controlling the level under different operating regions. Simulation studies carried out on MATLAB/SIMULINK platform reveal that the polynomial controllers can effectively be employed for multimodel approach in case of nonlinear systems.

Secured and Lossless Watermarking Technique for Patent Infringement

As digital data, digital maps are easy to update, duplicate, and distribute. At the same time, illegal duplication and distribution or forgery of the maps is also easy. This paper proposes a digital watermarking approach used for Patent Infringement. This methodology uses new shuffling technique, composition & decomposition, and encryption & decryption to record the information of a protected primary image and the allied watermarks. The quadtree can aid the processing of watermark and Rijndael provides added security to information. Besides that, we intend a novel architecture for patent protection that holds promise for a better compromise between quality and security for emerging digital rights management application.

Identification and Control of Air flow Process

This paper deals with identification and control of a non linear Air flow process. The objective of the present work is to control the flow rate of air through the process pipe line. The process is identified as a first order plus time delay process using real time data. The Model validation is also done. The PI controllers parameters are tuned using conventional and optimization techniques. The controller parameters are optimized using genetic algorithm (GA). The search range for optimization is based on the set of stabilizing controllers. The performances of the controllers are tested both in simulation and in real time.

Wavelet packet medical image coding using Space Frequency Quantization

Imaging modalities such as computerized tomography and magnetic resonance imaging produce digital images, which suffer from storage and transmission difficulties. Hence the need for high performance compression algorithms to reduce storage and transmission costs is evident. Wavelet transform is a class of subband coding of images, but it often fails to accurately capture high frequency information, especially at low bit rates where such information is lost in quantization noise. An alternate to wavelet transform is a wavelet packet that is better able to represent high-frequency information. The problem of wavelet packet image coding consists of considering all possible wavelet packet bases in the library and choosing the one that gives the best coding performance. In this work we use Space Frequency Quantization (SFQ) coder that undertaken the joint optimization of the spatial zerotree quantization and the step size of the scalar frequency quantizer. We compare the performance of wavelet packet SFQ coder with that of Vector Quantization (VQ) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Extensive experimental results show that SFQ coder gives excellent coding performance for Peak Signal to Noise Ratio (PSNR) values at various bit rates.